Understanding and improving clinicians' ability to manage critical events is important for patient safety. A team of clinician-scientists, simulation educators, and cognitive scientists propose a five-site in-depth study of the real-time action-oriented decision- making strategies of anesthesiologists during generalizable high-fidelity critical event simulations. The Specific Aims are to: 1) Develop and test a unified cognitive model and taxonomy of the decision-making strategies clinicians use during critical event management; 2) Create detailed profiles of participants' clinical practice and simulation experience; 3) Evaluate the factors affecting physicians' critical event performance; and 4) Evaluate the relationship between simulation-based performance assessment and existing metrics of physician competence (participants' primary board certification exam scores). We will create four 20-minute realistic simulations of critical events that are relevant to many acute-care specialties. 120 anesthesiologists at varying career points will provide detailed demographic, clinical practice, and simulation experience data. They will wear head-mounted video cameras as they perform in all four scenarios. Success of each participant's event management will be assessed later from overhead videos by independent trained experts based on timely completion of pre-defined `critical performance elements' and global technical and non-technical scores. After each scenario, cognitive interviews will be conducted using video-cued recall to ascertain how and why they made their clinical management decisions. We will iteratively code and analyze all of the interview data to develop a taxonomy of decision strategies; Every scenario will be coded for the strategies used. We will then analyze how event performance ratings vary with: participants' actual practice and simulation experience, the decision strategies used, and written and oral board certification examination scores. This innovative study will delineate in depth the thought processes of a diverse sample of physicians in managing critical events. Along with these empirical data, the project will create a new model and taxonomy for dynamic clinical decision-making, extending models previously developed for healthcare and other arenas of similar cognitive demands (e.g. aviation). The study will yield best-practice guidelines on decision-making in critical event management and information to support policymaking about the content, execution, and timing of simulation-based training and assessment.